Update README.md

## Overview.

**ProcessVenue’s** internal annotation team created a sarcasm detection dataset consisting of 5,940 real-world sentences, labelled under a strict double-blind dual-annotation strategy. The task focused on accurately separating Sarcastic text (ironic contradiction between literal meaning and intent) from non-sarcastic text (direct meaning).

Despite the inherently subjective and context-light nature of sarcasm, the project achieved strong individual correctness (~74–75%) and a stable Inter-Annotator Agreement (IAA) of 63.92%, producing a dependable training/evaluation corpus for sarcasm classification models.

README.md

@@ -8,7 +8,7 @@ tags:
 - sentiment-classification
 - nlp
 - sarcasm
-pretty_name: ProcessVenue Sarcasm Detection Dataset
+pretty_name: ProcessVenue Sarcasm Detection
 size_categories:
 - 1K<n<10K
 ---